Psychological attributes of house facades: A graph network approach in environmental psychology
Bibliographic record
Abstract
The interaction between humans and their living environments is of ubiquitous nature, however the relationship between environmental psychology and cognitive neuropsychology has scarcely been explored. Environmental psychology has approached the human-environment relation through psychological concepts such as affordance, attachment, identity, safety, or aesthetic preference. Cognitive neuropsychology, on the other hand, frequently uses houses as stimuli in object- and face-processing research. This study combines concepts from the two disciplines (cognitive neuropsychology and environmental psychology) to explore the interrelation between the architecture of living environments, human cognition and emotion. Using network analysis, we aimed to examine the underlying relationship between self-reported psychological attributes, and to explore the role of differences between three countries in appraisals of house facades. To that end, we used 50 images of house facades selected from a well-controlled data set of Canadian houses (DalHouses). 305 participants from Denmark, Germany, and Canada rated the houses on 12 psychological attribute dimensions. Results showed the highest strength for the nodes “friendliness”, “likability”, “invitingness”, “safety”, and “freedom”, while “typicality” and “familiarity” were unexpectedly low in strength. In terms of cultural differences, the three countries diverged in some correlations, e.g. facelikeness and loneliness, loneliness and freedom, but not in others, e.g. typicality and safety. Similar to the DalHouses study, "facelikeness" was not correlated with “typicality” nor with “liking”. Taken together, the present results identify psychological attributes that cluster together and could be particularly important in the characterization of house facades on a universal level, but also specific to different cultural and geographical backgrounds.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".